> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Training Recipes

## Docs

- [Training Recipes](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/overview.md): Choose a reusable NeMo AutoModel training workflow and find example configurations for fine-tuning, pretraining, distillation, retrieval, and diffusion models.
- [Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/sft-peft.md): Fine-tune language models with NeMo AutoModel using SFT or PEFT (LoRA), from install and YAML config through training, evaluation, publish, and vLLM deploy.
- [Pretrain Megatron Core Datasets with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/pretraining.md): 
- [Knowledge Distillation with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/knowledge-distillation.md): 
- [Sequence Classification (SFT/PEFT) with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/sequence-classification.md): Train a sequence classification model with NeMo AutoModel using GLUE MRPC, RoBERTa, and optional LoRA.
- [Retrieval Fine-Tuning (Bi-Encoder and Cross-Encoder)](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/retrieval-finetune.md): Fine-tune bi-encoder and cross-encoder retrieval models in NeMo AutoModel with distributed training, low-rank adaptation (LoRA), hard-negative mining, and evaluation.
- [Fine-Tune Large MoE LLMs](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/large-moe-fine-tuning.md): Fine-tune large Mixture-of-Experts (MoE) LLMs with NeMo AutoModel using HellaSwag recipes for MiniMax-M2.5, GLM-5, Step-3.5 Flash, and DeepSeek-V3.2.
- [Diffusion Model Fine-Tuning with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/diffusion-fine-tuning.md): Fine-tune text-to-image and text-to-video diffusion models with NeMo AutoModel using flow matching, from dataset prep through multi-node training and inference.
- [dLLM Fine-Tuning](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/dllm-fine-tuning.md): Fine-tune diffusion language models (dLLMs), including LLaDA, DiffusionGemma, DFlash, and I-DLM, with NeMo AutoModel.
- [Quantization-Aware Training (QAT) in NeMo Automodel](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/qat.md): 
- [Train an EAGLE Drafter for Speculative Decoding — End-to-End Guide](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/eagle-speculative-decoding.md): Step-by-step guide for training EAGLE-3 and EAGLE-1 speculative decoding drafters with NeMo AutoModel, from data preparation through SGLang serving.
- [Train a DFlash Drafter for Speculative Decoding](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/dflash-speculative-decoding.md): Train a DFlash block-parallel speculative-decoding drafter with NeMo AutoModel, from data through multi-GPU training, including the DFlash 2, Domino, and JetSpec variants.
- [Train a DSpark Drafter for Speculative Decoding](https://docs.nvidia.com/nemo/automodel/v0.7/recipes-e2e-examples/dspark-speculative-decoding.md): Train a DSpark semi-autoregressive speculative-decoding drafter with NeMo AutoModel, from data through 2-GPU FSDP2 training.